6 EC
Semester 1, period 2
5234NDDL6Y
This course constitutes an introduction to the field of computational neuroscience, focused on models of neural dynamics and bio-inspired approaches of deep learning networks. The course has both a theoretical component (lectures and seminar) and a practical hands-on component in which students will work in groups on specific computational projects.
Dayan and Abbott, “Theoretical Neuroscience”, MIT Press, 2001 (free version available online)
Gerstner, Kistler, Naud and Paninski, “Neuronal Dynamics: From Single Neurons to Networks and
Models of Cognition”, Cambridge Press, 2014 (freely available online)
Izhikevich, “Dynamical systems in neuroscience: the geometry of excitability and bursting” MIT
Press, 2007
-Lectures and seminars provides the students with the fundamental knowledge of computational neuroscience. Examples include neuron models, firing rate and spiking neural networks, and learning rules. The knowledge is deepened during project development and presentation/discussions.
-Computer sessions/practical training is designed to guide the students during the first steps of the research projects, via guidance from experience tutors. Students are encouraged to ask questions and discuss with their tutors in depth about any aspect of their group project.
-Students spend time working independently on their group project, and are assisted by their group tutor who provides feedback in daily meetings.
-Presentations (two interim and one final) give students the opportunity to present the progress/results of their group project and discuss with other tutors and students in the course.
|
Activity |
Hours |
|
|
Lectures and seminar |
40 |
|
|
Computer practical and feedback meetings |
48 |
|
|
Presentations |
12 |
|
|
Self study |
68 |
|
|
Total |
168 |
(6 EC x 28 uur) |
Requirements of the programme concerning attendance (OER-B):
Additional requirements for this course:
Attendance is required for all lectures, seminars and presentations. Absence needs to be communicated and discussed with the course coordinator.
| Item and weight | Details |
|
Final grade | |
|
0.1 (10%) 1st interim presentation | Mandatory |
|
0.1 (10%) 2nd interim presentation | Mandatory |
|
0.3 (30%) Final presentation | Mandatory |
|
0.5 (50%) Written report | Mandatory |
Feedback for each presentation is provided during the discussion section right after each one. Students will be provided with addiitonal feedback shortly (maximum two days delay) after each group presentation, together with their corresponding grade, and are instructed to reach their tutor if they require feedback on their performance. The written report grade will be accompanied by feedback, and students are instructed to request an inspection of the assessment at that moment.
-Two interim group presentations and one final presentation, during the second, third and four week of the course respectively. The presentations are graded and feedback is provided during the discussion following presentations and also together with the grade announcement up to two days later.
-One individualized written report, to be submitted in the last day of the course. The report is individually graded and feedback is provided together with the grade.
The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl
| Week | Subject |
| 1 | Lectures on neural dynamics |
| 1 | Lectures on deep learning |
| 1 | Computational practice |
| 2 | Seminars on computational neuroscience topics |
| 2 | Practical and feedback meetings |
| 2 | 1st interim presentation |
| 3 | Seminars on computational neuroscience topics |
| 3 | Practical and feedback meetings |
| 3 | 2nd interim presentation |
| 4 | Practical and feedback meetings |
| 4 | Final presentation |
| 4 | Written report deadline |
Students interested in joining the course and signed up via Datanose will be contacted from the course coordinator to request selection materials in case a selection needs to be done.